Associating taste with consumable records

ABSTRACT

A method for operating a health tracking system, a health tracking system, and non-transitory computer-readable medium for operating a health tracking system are disclosed. The method comprises receiving a data record comprising at least a descriptive string and nutritional data regarding a consumable item to which the data record corresponds; determining a taste associated to the consumable item based on an evaluation of at least one of: (i) the descriptive string, and (ii) the nutritional data; and associating the determined taste with the data record in a database.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims the benefit of U.S.patent application Ser. No. 15/215,861, filed Jul. 21, 2016, the entirecontents of which are incorporated herein by reference.

COPYRIGHT

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever.

FIELD

The method and apparatus disclosed in this document relates health andfitness tracking systems and, more particularly, to associating a tastewith food and other consumable records in a database.

BACKGROUND

In recent years, health and fitness tracking applications that trackfood consumption have become very popular. Food consumption is importantto a healthy lifestyle and is known to be related to various healthconditions, such as diabetes and obesity to name a few. Health andfitness tracking applications allow users to set and achievepersonalized health goals by tracking the foods and beverages that theyconsume. These applications enable users to gain insights that help themmake smarter choices and create healthier habits.

Although many factors like colors, texture, temperature, and crushingsound play an important role in food sensation and consumption, it isstrongly believed that food taste is one of the most important factorsrelated to palatability, as well as to overall food consumptionbehavior. More specifically, taste is one of the primary drivers of foodchoice. Accordingly, any attempt to guide users towards healthier eatinghabits requires a clear picture of food tastes both on the individualand aggregate levels.

Hence, it would be advantageous to provide a method for predicting ataste of a food item and automatically labeling food items in a databasewith the predicted tastes. It would also be advantageous if the methodis easily scalable for very large databases and robust enough to handleuser-generated records having potentially inaccurate information.

SUMMARY

In accordance with one exemplary embodiment of the disclosures, a methodof operating a health tracking system is disclosed. The method comprisesreceiving a data record comprising at least a descriptive string andnutritional data regarding a consumable item to which the data recordcorresponds; determining a taste associated to the consumable item basedon an evaluation of at least one of: (i) the descriptive string, and(ii) the nutritional data; and associating the determined taste with thedata record in a database.

Pursuant to another exemplary embodiment of the disclosures, a healthtracking system is disclosed. The system comprises a database configuredto store a plurality of data records, each of the plurality of datarecords comprising at least a descriptive string and nutritional dataregarding the consumable item to which the data record corresponds; anda data processor in communication with the database, the data processorbeing configured to (i) receive one or more of the plurality of datarecords from the database, and (ii) determine a taste aspect for theconsumable item to which each of the received ones of the one or more ofthe plurality of data records corresponds, the determination being basedon an evaluation of at least one of the descriptive string and thenutritional data.

In accordance with yet another exemplary embodiment, a non-transitorycomputer-readable medium for a health tracking system is disclosed. Thecomputer-readable medium has a plurality of instructions stored thereonthat, when executed by a processor, cause the processor to: receive aplurality of data records from a database, the plurality data recordseach comprising a descriptive string and nutritional data regarding therespective corresponding consumable; determine a taste for each of therespective corresponding consumables of the plurality of data recordsbased on at least one of (i) the descriptive string and (ii) thenutritional data thereof; and store the determined tastes in thedatabase associated to the respective ones of the plurality of datarecords.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing aspects and other features of a health and fitnesstracking system are explained in the following description, taken inconnection with the accompanying drawings.

FIG. 1 shows a health tracking system.

FIG. 2 shows a system server or data processing system of the healthtracking system of FIG. 1.

FIG. 3 shows a smartphone of the health tracking system of FIG. 1.

FIG. 4 shows a method of providing taste information for consumablerecords in a database.

FIG. 5 shows a method for determining a taste of a consumable.

FIG. 6 shows a method of applying a text-based model to calculateprobabilities that a consumable has each of the possible tastes.

FIG. 7 shows a method of applying a nutrient-based model to calculateprobabilities that a consumable has each of the possible tastes.

FIG. 8 shows exemplary correlations between taste preferences anddifferent age groups.

FIG. 9 shows exemplary correlations between taste preferences andobserved BMI and gender.

FIG. 10 shows exemplary taste preferences across meals.

FIG. 11 shows exemplary taste preferences across countries.

All Figures © Under Armour, Inc. 2016. All rights reserved.

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principles of thedisclosure, reference will now be made to the embodiments illustrated inthe drawings and described in the following written specification. It isunderstood that no limitation to the scope of the disclosure is therebyintended. It is further understood that the present disclosure includesany alterations and modifications to the illustrated embodiments andincludes further applications of the principles of the disclosure as maynormally occur to one skilled in the art which this disclosure pertains.

Disclosed embodiments include systems, apparatus, and methods associatedwith health and fitness tracking in general, and in particular a systemfor associating a taste with food records in a database.

In the following detailed description, reference is made to theaccompanying drawings which form a part hereof wherein like numeralsdesignate like parts throughout, and in which is shown, by way ofillustration, embodiments that may be practiced. It is to be understoodthat other embodiments may be utilized, and structural or logicalchanges may be made without departing from the scope of the presentdisclosure. Therefore, the following detailed description is not to betaken in a limiting sense, and the scope of embodiments is defined bythe appended claims and their equivalents.

Aspects of the disclosure are disclosed in the accompanying description.Alternate embodiments of the present disclosure and their equivalentsmay be devised without parting from the spirit or scope of the presentdisclosure. It is noted that any discussion herein regarding “oneembodiment”, “an embodiment”, “an exemplary embodiment”, and the likeindicate that the embodiment described may include a particular feature,structure, or characteristic, and that such particular feature,structure, or characteristic may not necessarily be included in everyembodiment. In addition, references to the foregoing do not necessarilycomprise a reference to the same embodiment. Finally, irrespective ofwhether it is explicitly described, one of ordinary skill in the artwill readily appreciate that each of the particular features,structures, or characteristics of the given embodiments may be utilizedin connection or combination with those of any other embodimentdiscussed herein.

Various operations may be described as multiple discrete actions oroperations in turn, in a manner that is most helpful in understandingthe claimed subject matter. However, the order of description is not tobe construed as to imply that these operations are necessarily orderdependent. In particular, these operations may not be performed in theorder of presentation. Operations described may be performed in adifferent order than the described embodiment. Various additionaloperations may be performed and/or described operations may be omittedin additional embodiments.

For the purposes of the present disclosure, the phrase “A and/or B”means (A), (B), or (A and B). For the purposes of the presentdisclosure, the phrase “A, B, and/or C” means (A), (B), (C), (A and B),(A and C), (B and C), or (A, B and C).

The terms “comprising,” “including,” “having,” and the like, as usedwith respect to embodiments of the present disclosure, are synonymous.

As used herein, the term “consumable” as refers to foods, beverages,dietary supplements, vitamin supplements, medication, and other itemsfor consumption. As used herein, the term “consumable record” refers toa database record that relates to a particular consumable. Eachconsumable record comprises a plurality of data fields that relate to aparticular consumable. In some embodiments, the consumable recordincludes a description field that includes data, such as a text string,that identifies or describes the particular consumable. In someembodiments, each consumable record includes fields for caloric content,macronutrients, micronutrients, serving size, and other nutrition andhealth information.

Health Tracking System

With reference to FIG. 1, an exemplary embodiment of a health trackingsystem 100 including automatic taste prediction and labeling is shown.In the illustrated embodiment, the health tracking system 100 includes aplurality of health tracking devices 110 in communication with a systemserver 200 or other data processing system over a network 120 such as,e.g. the Internet.

The server 200 comprises a computerized device or data processing systemconfigured to run one or more software applications on a processorthereof (e.g. the network-side health tracking program 218). The server200 of the present embodiment is further configured to receive aplurality of consumable records which include caloric and nutritionalcontents of a respective plurality of consumable items which are enteredat the health tracking devices 110, other consumer devices, and/orprovided from one or more manufacturing or distributing entities. Theconsumable records are stored at a storage apparatus or memory of theserver 200 (e.g., consumable records 224).

The storage apparatus or memory is configured to store instructionsincluding a network-side health tracking program 218 (which may also bereferred to herein as the “health tracking application”), as well as adatabase 220 accessible by at least the health tracking program 218. Thedatabase 220 includes user data 222, consumable records 224, operationalrecords 226, and graphics 228. Alternatively, the server 200 may be incommunication with a separate storage entity (not shown) for storagethereof. The server 200 is configured to automatically determine a tastefor a consumable corresponding to each of the consumable records 224, asdiscussed in further detail elsewhere herein. The determined tastes arestored in the database 220 in association with corresponding consumablerecords 224. In one variant, each of the consumable records 224advantageously includes a label which identifies a “taste” for thecorresponding consumable. The health tracking system 100 is configuredto utilize these so-called “taste labels” to enable functionalities andfeatures discussed in further detail herein.

The health tracking devices 110 (which may also be referred to herein as“health and fitness tracking devices”) comprise any number ofcomputerized apparatus which include a user interface such as e.g., asmartphone 110A, laptop computer 110B, a tablet computer, a desktopcomputer 110C, or other such device. In at least one embodiment, theuser interface may comprise an LCD touch screen or the like, a mouse orother pointing device, a keyboard or other keypad, speakers, and amicrophone, as will be recognized by those of ordinary skill in the art.The user interface provides the user with any of various health, fitnessand activity related data such as food and nutritional consumption,calorie expenditure, sleep metrics, weight, body fat, heart rate,distance travelled, steps taken, etc. In order to connect to the network120, the health tracking devices 110 are generally configured to utilizeany of various wired or wireless communications components,infrastructures and systems, such as cell towers 115 of a mobiletelephony network, wireless routers 125, Bluetooth®, near fieldcommunication (NFC), or physical cables. Health tracking devices 110 mayuse data collected from sensors associated to or in communication withthe health tracking device 110, such as heart rate monitors, stepcounters, stair counters, global positioning system (“GPS”) trackingdevices, as well as various other motion tracking and biometricmonitoring devices; alternatively, or in addition, a user may manuallyenter health related data. Such sensors allow the user to easily trackand automatically log activity and/or consumption information with thehealth tracking device.

The health tracking devices 110 are configured to communicate with thesystem server 200 in order to enable: accessing and searching of theconsumable records 224 stored thereat, display of the consumablerecords, provide additional records, and/or enable the user to selectindividual ones of the displayed consumable records for the purposes ofcaloric and nutritional logging. In one embodiment, foregoing functionsare performed via execution of one or more software applications at theserver 200 (i.e., server or network-side applications) in communicationwith one or more complementary software applications at the healthtracking devices 110 (i.e., client-side applications). For example, thehealth tracking program 218, running on the processor (of the server200) may be utilized to accomplish the foregoing, as explained infurther detail below. A client-side software application for performingvarious functions necessary for the herein disclosed concepts may alsobe utilized (see health tracking application 316 of FIG. 3, discussedbelow).

System Server

With reference now to FIG. 2, a block diagram of an exemplary embodimentof the system server 200 of FIG. 1 is shown. It is appreciated that theembodiment of the system server 200 shown in FIG. 2 is only oneexemplary embodiment of a system server 200. As such, the exemplaryembodiment of the system server 200 of FIG. 2 is merely representativeof any of various manners or configurations of system servers or otherdata processing systems that are operative in the manner set forthherein.

The system server 200 of FIG. 2 is typically provided in a housing,cabinet or the like 202 that is configured in a typical manner for aserver or related computing device. In one embodiment, the system server200 includes processing circuitry/logic 204, memory 206, a power module208, a user interface 210, a network communications module 212, and awireless transceiver 214.

The processing circuitry/logic 204 is operative, configured and/oradapted to operate the system server 200 including the features,functionality, characteristics and/or the like as described herein. Tothis end, the processing circuitry/logic 204 is operably connected tothe memory 206, the power module 208, the user interface 210, thenetwork communications module 212, and the wireless transceiver 214. Thememory 206 may be of any type of device capable of storing informationaccessible by the processor, such as a memory card, ROM, RAM,write-capable memories, read-only memories, hard drives, discs, flashmemory, or any of various other computer-readable medium serving as datastorage devices as will be recognized by those of ordinary skill in theart. The memory 206 is configured to store instructions including anetwork-side health tracking application 218 for execution by theprocessing circuitry/logic 204, as well as a database 220 for use by atleast the health tracking program 218. The database 220 includes userdata 222, consumable records 224, operational records 226, and graphics228. As discussed in greater detail below, the health trackingapplication 218 includes a taste determination engine 230 configured todetermine tastes for consumables and provide taste labels that arestored in association with each consumable record 224.

With continued reference to FIG. 2, the power module 208 of the systemserver 200 is operative, adapted and/or configured to supply appropriateelectricity to the system server 200 (i.e., including the variouscomponents of the system server 200). The power module 208 may operateon standard 120 volt AC electricity, but may alternatively operate onother AC voltages or include DC power supplied by a battery orbatteries.

The network communication module 212 of the system server 200 providesan interface that allows for communication with any of various devicesusing various means. In particular, the network communications module212 includes a local area network port that allows for communicationwith any of various local computers housed in the same or nearbyfacility. In some embodiments, the network communications module 212further includes a wide area network port that allows for communicationswith remote computers over the Internet (e.g., network 120 of FIG. 1).Alternatively, the system server 200 communicates with the network 120via a modem and/or router of the local area network. In one embodiment,the network communications module is equipped with a Wi-Fi transceiver214 or other wireless communications device. Accordingly, it will beappreciated that communications with the system server 200 may occur viawired communications or via the wireless communications. Communicationsmay be accomplished using any of various known communications protocols.In the embodiment of FIG. 2, the wireless transceiver 214 may be a Wi-Fitransceiver, but it will be recognized that the wireless transceiver mayalternatively use a different communications protocol.

The system server 200 may be accessed locally by an authorized user(i.e., an administrator or operator). To facilitate local access, thesystem server 200 includes an interactive user interface 210. Via theuser interface 210, the health tracking application 218, and may collectdata from and store data to the memory 206. In at least one embodiment,the user interface 210 may suitably include an LCD touch screen or thelike, a mouse or other pointing device, a keyboard or other keypad,speakers, and a microphone, as will be recognized by those of ordinaryskill in the art. Accordingly, the user interface 210 is configured toprovide an administrator or other authorized user with access to thememory 206 and allow the authorized user to amend, manipulate anddisplay information contained within the memory.

As mentioned above, the memory 206 includes various programs and otherinstructions that may be executed by the processor circuitry/logic 204.In particular, the memory 206 of the system server 200 of FIG. 2includes the health tracking program 218 (which may also be referred toherein as a “health tracking application”). The health tracking program218 is configured to cause the system server 200 to enable a user toobtain nutritional data related to any of various consumables. Executionof the health tracking application 218 by the processor circuitry/logic204 results in signals being sent to and received from the userinterface 210 and the communications module 212 (for further delivery toa user device such as a health tracking device 110), in order to allowthe user receive and update various aspects of the consumable records224. The network-side health tracking application 218 is configured toprovide various graphical views and screen arrangements to be displayedto a user on a health tracking device 110.

The user data 222 includes at least user profiles 232 and correspondingconsumable logs 234. The user profiles 232 include a profile data foreach user of the health tracking system 100. Each user profile includesdemographic information for the users such as name, age, gender, height,weight, performance level (e.g., beginner, intermediate, professional,etc.) and/or other information for the user. In at least one embodiment,the consumable logs 234 include a consumable diary/log for each user.The consumable diary/log allows the user to track consumables that areconsumed by the user over a period of days and any nutritional dataassociated with the food consumed. For example, the consumable diary/logmay allow the user to enter particular consumable that is consumed bythe user and keep track of the associated calories, macronutrients,micronutrients, sugar, fiber, and/or any of various other nutritionaldata associated with the consumables entered by the user in theconsumable diary/log. In some embodiments, the user data 222 furtherincludes various activity and fitness data collected by sensors (notshown) associated with the health tracking devices 110.

In an alternative embodiment, the foregoing profile data may be storedat a storage entity separate from yet in communication with the server200. For example, a centralized server may be provided which isconfigured to store all data relating to an individual user in onestorage area (including workout data, nutrition/consumption data,profile data, etc.).

A plurality of consumable records 224 are stored in the database 220. Asdiscussed above, the term “consumable record” refers to a databaserecord that relates to a particular consumable. In at least oneembodiment, each consumable record comprises a plurality of data fieldsthat related to a particular consumable. At least some consumablerecords 224 and/or fields are editable by users or may be created byusers within the database 220 without the need for special authorizationor privileges. In the disclosed embodiment, each of the consumablerecords includes a number of fields including, for example, a name forthe consumable, summary information about the consumable, and detailednutritional information about the consumable. Detailed information abouta consumable may include one or more of: serving size, calories,ingredients, or any other nutritional information about the consumable.For example, the nutritional information may include information thatmay be provided on USDA food labels or state-regulated food labels(e.g., vitamin and mineral content, fat content, cholesterol content,protein content, sugar content, carbohydrate content, fiber content,organic contents, etc.). The summary information about the consumablemay include some subset of the more detailed information about theconsumable. For example, the summary information about the consumablemay only include serving size and calorie information. The variousfields of each consumable record may be populated by data from any useror third party data providers. Therefore, it will be recognized that inat least some embodiments, consumable records 224 may have been enteredby any of various sources including an administrator or operator of thehealth tracking system 100, commercial food providers (e.g., fooddistributors, restaurant owners, etc.), and/or users of the healthtracking system 100.

The operational records 226 include current and historical data storedby the system server 200 in association with operation of the systemserver 200, execution of the health tracking application 218, and/ormanipulation of data 220 within the memory 206. For example, theoperational records 226 may include information concerning amendmentsmade to any of various consumable records 224. The operational records226 may also include other information related to the control andoperation of the system server 200, including statistical, logging,licensing, and historical information.

In one embodiment, graphical views 228 are provided at the server 200which are pushed to the health tracking device 110 for display thereatof various screen arrangements, as shown in FIG. 1.

While the system server 200 has been explained in the foregoingembodiment as housing the health tracking program 218 and the variousrecords and databases in the memory 206, it will be recognized that inother embodiments these components may be retained in other one or moreremote locations in communication with the health tracking system 100.For example, in at least one embodiment, the consumable records 224 maybe data retained by a database separate from the system server 200.Alternatively, the consumable records 224 or certain fields of theconsumable records 224 are received from a third party database. In suchembodiments, the health tracking application may utilize any number ofapplication programming interfaces (APIs) to access the data in thethird party databases and incorporate such information for use in thehealth tracking application 218, without local storage thereof.Accordingly, it will be recognized that the description of the systemserver 200 of FIG. 2 is but one exemplary embodiment of a dataprocessing system that may be utilized by the health tracking system100.

A computer program product implementing an embodiment disclosed hereinmay therefore comprise one or more computer-readable storage mediastoring computer instructions executable by a processor to provide anembodiment of a system or perform an embodiment of a method disclosedherein. Computer instructions (e.g., the health tracking application 218including the taste determination engine 230) may be provided by linesof code in any of various languages as will be recognized by those ofordinary skill in the art. A “non-transitory computer-readable medium”may be any type of data storage medium that may store computerinstructions, including, but not limited to a memory card, ROM, RAM,write-capable memories, read-only memories, hard drives, discs, flashmemory, or any of various other computer-readable medium.

Health Tracking Devices

With reference again to FIG. 1, the health tracking devices 110 may beprovided in any of various forms. Examples of a health tracking devices110 configured for use with the health tracking system 100 include asmartphone 110A, a laptop computer 110B, and a desktop computer 110C, asshown in FIG. 1, as well as various other electronic devices.Accordingly, it will be recognized that the health tracking devices 110may comprise portable electronic devices such as the smartphone 110A orthe laptop computer 110B, or stationary electronic devices such as thedesktop computer 110C. Other examples of health tracking devicesinclude, handheld or tablet computers, smart watches, portable mediaplayers, other wearable devices, or any of various other health trackingdevices configured to receive entry of consumables (not shown).

In one embodiment, data entered at one device 110 may be provided toother ones of the user's devices 110. For example, data entered at thesmart phone 110A may be provided to the desktop computer 110C and/or thelaptop computer 110B for storage thereat. Alternatively, the data may bestored at a single network storage apparatus (not shown) having adedicated portion of storage for records relating to the user andaccessible by all of the user's devices 110.

With reference now to FIG. 3, in at least one embodiment the healthtracking device 110 is provided in the form of a smartphone 110A. Thesmartphone 110A includes a display screen 302, an input/output (I/O)interface 304, a processor 308, a memory 310, and one or moretransceivers 312. The smartphone 110A also includes a protective outershell or housing 414 designed to retain and protect the electroniccomponents positioned within the housing 414. The smartphone 110A alsoincludes a battery (not shown) configured to power the display screen302, processor 308, transceivers 312 and various other the electroniccomponents within the smartphone 110A.

The display screen 302 of the smartphone 110A may be an LED screen orany of various other screens appropriate for the personal electronicdevice. The I/O interface 304 of the smartphone 110A includes softwareand hardware configured to facilitate communications with the user. TheI/O interface 304 is in communication with the display screen 302 and isconfigured to visually display graphics, text, and other data to theuser via the display screen 302. As will be recognized by those ofordinary skill in the art, the components of the health tracking device110 may vary depending on the type of display device used. Alternativehealth tracking devices, such as the laptop 110B and the desktop 110C,may include much of the same functionality and components as thesmartphone 110A shown in FIG. 3, but may not include all the samefunctionality or components and/or may include others not listed.

The processor 308 of the smartphone 110A may be any of variousprocessors as will be recognized by those of ordinary skill in the art.The processor 308 is in communication with the I/O interface 304, thememory 310, and the transceivers 312, and is configured to deliver datato and receive data from each of these components. The memory 310 isconfigured to store information, including data and instructions forexecution by the processor 308. It will be recognized by those ofordinary skill in the art that a “processor” includes any hardwaresystem, hardware mechanism or hardware component that processes data,signals or other information. A processor may include a system with acentral processing unit, multiple processing units, dedicated circuitryfor achieving functionality, or other systems.

The transceivers 312 may be any of various devices configured forcommunication with other electronic devices, including the ability tosend communication signals and receive communication signals. Thetransceivers 312 may include different types of transceivers configuredto communicate with different networks and systems. Such transceiversare well known and will be recognized by those of ordinary skill in theart.

In some embodiments, the transceivers 312 include at least onetransceiver configured to allow the smartphone 110A to perform wirelesscommunications with the cell towers 115 of the wireless telephonynetwork, as will be recognized by those of ordinary skill in the art.The wireless telephony network may comprise any of several known orfuture network types. For example, the wireless telephony network maycomprise commonly used cellular phone networks using CDMA, GSM or FDMAcommunication schemes, as well as various other current or futurewireless telecommunications arrangements.

In some embodiments, the transceivers 312 include at least onetransceiver configured to allow the smartphone 110A to communicate withany of various local area networks using Wi-Fi, Bluetooth® or any ofvarious other communications schemes.

In some embodiments, the memory 310 includes program instructions for agraphical user interface configured to provide a client-side healthtracking application 316. The memory 310 may further be configured tostore certain user data 318, such as e.g., user gender, height, weight,user identifier, password, etc. Additionally, health related data (e.g.,data collected from one or more sensors and/or manually entered) may bestored. The processor 308 is configured to read the program instructionsfrom the memory 310 and execute the program instructions to provide thehealth tracking application 316 to the user so for the purpose ofperforming health and fitness related tasks for the user, includingdisplaying, modifying, and analyzing the user data 318.

In at least one embodiment, the user data 318 includes a plurality ofconsumable records which serves as a log of consumables that have beenconsumed by the user for the purpose of caloric and nutritionaltracking. That is to say, the client-side health tracking application316 is configured to display consumable records and enable the user toselect consumable records (from a plurality of records accessed via thenetwork 120), those items that correspond to consumables that he or shehas consumed are stored at the client-side for the purpose of loggingthe consumables in this embodiment. In another alternative, such log maybe stored remote from the device and/or only kept at the device for atransitory period.

The memory 310 that retains the data and instructions may be of any typeof device capable of storing information accessible by the processor,such as a memory card, ROM, RAM, write-capable memories, read-onlymemories, hard drives, discs, flash memory, or any of various othercomputer-readable medium serving as data storage devices as will berecognized by those of ordinary skill in the art. Portions of the systemand methods described herein may be implemented in suitable softwarecode that may reside within the memory as software or firmware.Alternatively, or in addition, the software (such as e.g., the clientside health tracking program 316) may be downloaded from a networklocation, such as via the Internet.

Method of Predicting Taste for Consumable Records

Methods for operating the health tracking system 100 are describedbelow. In particular, a method of associating taste information withfood records is provided. In the description of the methods, statementsthat a method is performing some task or function refers to a controlleror general purpose processor executing programmed instructions stored innon-transitory computer readable storage media operatively connected tothe controller or processor to manipulate data or to operate one or morecomponents in the health tracking system 100 to perform the task orfunction. Particularly, the processor circuitry/logic 204 of the systemserver 200 and/or the processor 308 of the smartphone 110A above may besuch a controller or processor. Alternatively, the controller may beimplemented with more than one processor and associated circuitry andcomponents, each of which is configured to form one or more tasks orfunctions described herein. Additionally, the steps of the methods maybe performed in any feasible chronological order, regardless of theorder shown in the figures or the order in which the steps aredescribed.

FIG. 4 shows a method 400 of providing taste information for consumablerecords 224. The method 400 begins with a step of receiving a consumablerecord, the consumable record including a descriptive string for acorresponding consumable and nutritional data regarding thecorresponding consumable (block 410). Particularly, with respect to theembodiments described in detail herein, the processing circuitry/logic204 of the system server 200 is configured to receive or read aconsumable record from the database 220. The received consumable recordat least includes a descriptive string that provides a name for theconsumable (e.g., “apple”) and one or more nutritional data regarding acorresponding consumable. In a preferred embodiment, the nutritionaldata at least includes total caloric contents and macronutrient contents(i.e. fat content, carbohydrate content, protein content, etc.).

Next, the method 400 includes a step of determining a taste for thecorresponding consumable based on at least one of (i) the descriptivestring and (ii) the nutritional data (block 420). In one embodiment, theprocessing circuitry/logic 204 is configured to execute programinstructions of the taste determination engine 230 to determine a tastefor the consumable that corresponds to the received consumable record.The processing circuitry/logic 204 is configured to determine the tastefor the consumable based on the descriptive string of the receivedconsumable record and/or the one or more nutritional data of thereceived consumable record. In one embodiment, the taste is one or moreof the following fundamental flavors: bitter, salty, umami (savory),sour, spicy, and sweet; however other aspects of taste and/or ofconsumable items may be determined using the herein disclosed methodsand systems. Methods for determining a taste for a consumable arediscussed in greater detail below.

Finally, the method 400 includes a step of associating the determinedtaste with the received consumable record in a database (block 430).Particularly, the processing circuitry/logic 204 is configured to storethe determined taste for the consumable record in a data field of thedatabase 220 or otherwise in association with the received consumablerecord (e.g., in a taste data field of the consumable record).

In many embodiments, the method 400 is repeated with respect to eachconsumable record 224 in the database 220 having the data in the fieldsrequired by the taste determination engine 230 for determining a tasteof the corresponding consumable. In another embodiment, the method 400is performed automatically after a new consumable record is generated bythe health tracking system 100 or is otherwise received by the healthtracking system 100. Alternatively, the method 400 may be performed on aperiodic and/or scheduled basis to determine tastes for any newly addedor modified consumable records in the database 220.

Taste Determination Engine

FIG. 5 shows a method 500 of determining a taste for a consumable basedon data stored in the corresponding consumable record. The method 500 isone exemplary implementation of step 420 of the method 400 and anexemplary embodiment of the taste determination engine 230. However, itwill be understood by a person having ordinary skill in the art thatother methods and/or variations of this method are possible.

The method 500 begins by determining a plausibility or accuracy of thenutritional data of the consumable record (block 510). Particularly,with respect to the embodiments described in detail herein, theprocessing circuitry/logic 204 of the system server 200 is configured todetermine if the nutritional data is plausible or accurate by performingone or more checks on the nutritional data. In one embodiment, theprocessing circuitry/logic 204 is configured to compare the totalcaloric content of the consumable to the macronutrient contents of theconsumable to determine in the nutritional data is plausible oraccurate. More particularly, it is known that fat has approximately 9calories per gram, that protein has approximately 4 calories per gram,and that carbohydrates have 4 calories per gram. Accordingly, thenutritional data may be verified according to the equation: TotalCalories 4*Protein+4*Carbohydrates+9*Fats. If the estimatedmacronutrient calories are roughly equal to the listed total caloriccontent within a predetermined threshold, then the processingcircuitry/logic 204 is configured to determine that the nutritional datais plausible or accurate. However, if the estimated macronutrientcalories differ from the listed total caloric content by more than apredetermined amount or percentage, then the processing circuitry/logic204 is configured to determine that the nutritional data is implausibleor inaccurate.

In some embodiments, additional checks are performed to determine theplausibility or accuracy of the nutritional data. Particularly, in oneembodiment, the processing circuitry/logic 204 is configured to comparethe descriptive string of consumable to the macronutrient contents. Incertain limited circumstances, the descriptive string of the consumablemay give important clues about whether the nutritional data areaccurate. For example, if the descriptive string includes the phrase“protein shake” but the nutritional data indicates that the consumablehas zero grams of protein, then it may be assumed that the nutritionaldata is incorrect or incomplete Similarly, if the descriptive stringincludes the phrase “fried” but the nutritional data indicates that theconsumable has zero grams of fat, then it may be assumed that thenutritional data is incorrect or incomplete.

In further embodiments, the processing circuitry/logic 204 is configuredto compare a serving size for the consumable with the nutritionalcontents of the consumable. Particularly, if the serving size is listedin terms of mass, then the serving size is greater than (or, at the veryleast, equal to) a sum of the masses of fat, protein, carbohydrates, andfiber. For example, if the serving size is “32 grams”, but thenutritional data indicates that there are 22 grams of fat, 18 grams ofprotein, 29 grams of carbohydrates, and 4 grams of fiber per serving,then it may be assumed that the nutritional data is incorrect orincomplete. However, if the serving size is greater than the sum of themasses of fat, protein, carbohydrates, and fiber, then no inferences maybe drawn regarding the accuracy of nutritional content. It is noted thatthis comparison may not be made if the serving size is listed in termsof volume (e.g., fluid ounces).

If it is determined that the nutritional data is implausible orinaccurate, then the method 500 continues with a step of applying afirst model to the descriptive string (block 520). Particularly, theprocessing circuitry/logic 204 is configured to ignore the nutritionaldata and apply a text-based model (described in further detail belowwith respect to FIG. 6) for predicting a taste of the consumable usingthe descriptive string. The processing circuitry/logic 204 is configuredto use the text-based model to calculate a set of probabilities that theconsumable has each of the possible tastes (block 530). In oneembodiment, the possible tastes include bitter, salty, umami (savory),sour, spicy, and sweet. However, other tastes or consumable item relatedaspects may be utilized with equal success. Table 1, below, shows anexemplary sets of probabilities for several consumables:

TABLE 1 Descriptive String Probabilities (i.e. Food Name) Bitter SaltyUmami Sour Spicy Sweet Instant Coffee + 0.8079 0.0000 0.0000 0.00000.0000 0.1921 1 Sugar + 25 ml Skim Milk Caramel Popcorn 0.0000 0.42980.0000 0.0319 0.0000 0.5383 Fruit Smoothie 0.0000 0.0000 0.0000 0.13790.0000 0.8621 Spices - Pepper, 0.0274 0.0000 0.2119 0.0290 0.5532 0.1786red or cayenne Pepperoni Pizza 0.0000 0.1834 0.6806 0.0000 0.0579 0.0786Gummi Worms 0.0000 0.0000 0.0000 0.1456 0.0000 0.8544 Coke Zero 12 oz0.0000 0.0000 0.0000 0.0000 0.0000 1.0000

If it is determined that the nutritional data is plausible or accurate,then the method 500 continues with steps of applying a first model tothe descriptive string (block 520) and applying a second model to thenutritional data (block 525). Particularly, the processingcircuitry/logic 204 is configured to apply the text-based model forpredicting a taste of the consumable using the descriptive string andapply a nutrient-based model (described in further detail below withrespect to FIG. 7) for predicting a taste of the consumable using thenutritional data. The processing circuitry/logic 204 is configured touse the text-based model to calculate a first set of probabilities thatthe consumable has each of the possible tastes (block 530).Additionally, the processing circuitry/logic 204 is configured to usethe nutrient-based model to calculate a second set of probabilities thatthe consumable has each of the possible tastes (block 535).

In the case in which the nutritional data is plausible or accurate andin which both models are used to calculate separate sets ofprobabilities, the method 500 continues with a step of calculating finalweighted probabilities (block 540). Particularly, the processingcircuitry/logic 204 is configured to calculate a set of finalprobabilities based on a weighted average of the first set ofprobabilities provided by the text-based model and the second set ofprobabilities provided by the nutrient-based model. In one embodiment,the weighting comprises a predetermined or fixed weighting. However, inother embodiments, the weighting is dependent on the first set ofprobabilities calculated with respect to the text-based model.

For example, in one embodiment wherein the weighting is dependent on thefirst set of probabilities calculated with respect to the text-basedmodel, if the text-based model yields over a 50% probability that theconsumable is either “sour” or “spicy”, then the second set ofprobabilities calculated with respect to the nutrient-based model areignored or minimally weighted because nutrient content is minimallypredictive of sour and/or spicy tasting consumables. If the text-basedmodel yields over 50% probability that the consumable is any of“bitter”, “salty”, “umami”, or “sweet”, then the second set ofprobabilities calculated with respect to the nutrient-based model aremoderately weighted (e.g., with 30% weighting). Additionally, if thetext-based model is relatively non-informative with respect to tastethen the second set of probabilities calculated with respect to thenutrient-based model are more heavily weighted. For example, if thetext-based model yields somewhat non-informative probabilities, such asbitter-38%, salty-42%, umami-10%, sour-0%, spicy-0%, and sweet-0%, thenthe second set of probabilities calculated with respect to thenutrient-based model may be weighted with around 50% weightingSimilarly, if the text-based model yields very uninformativeprobabilities, such as bitter-16%, sailty-20%, umami-14%, sour-17%,spicy-18%, and sweet-15%, then then the second set of probabilitiescalculated with respect to the nutrient-based model may be weighted with100% weighting.

Once a final set of probabilities has been determined, the method 500continues with a step of determining a most likely taste for theconsumable (block 550). Particularly, the processing circuitry/logic 204is configured to select the taste having the highest probability score.As discussed above with respect to the step 430 of the method 400, theprocessing circuitry/logic 204 is configured to store the determinedtaste in the database 220, such as in a data field of, or otherwise inassociation with, the corresponding consumable record. In oneembodiment, if the two most likely tastes have probabilities that arewithin 20% (or 0.2) of each other, the processing circuitry/logic 204may be configured to determine that the taste for the consumable is“undecided”. In another embodiment, the processing circuitry/logic 204is configured to instead determine that the taste for the consumable isboth of two most likely tastes, thereby labeling the consumable with twotaste labels.

Text-Based Model

FIG. 6 shows a method 600 of applying a text-based model to calculateprobabilities that a consumable has each of the possible tastes. Themethod 600 is one exemplary implementation of steps 520 and 530 of themethod 500. However, it will be understood by a person having ordinaryskill in the art that other methods and/or variations of this method arepossible.

The method 600 begins with a descriptive string of a consumable recordas an input 610. The method continues with a step of parsing thedescriptive string (block 620). Particularly, with respect to theembodiments described in detail herein, the processing circuitry/logic204 of the system server 200 is configured to vectorize the descriptivestring, which is a type of parsing in which the resulting vector is arepresentation the component parts of the descriptive string and therelationships between those component parts. In one exemplaryembodiment, a vector model, such as the so-called word2vec model, isused to transform the descriptive string into a high-dimensional vector,or simply a point in high-dimensional space.

Next, the method 600 includes a step of applying a model to the parsed(or vectorized) descriptive string (block 630). Particularly, theprocessing circuitry/logic 204 is configured to compare the vectorizeddescriptive string to a training set of vectors and calculateprobabilities that the consumable has each of the possible tastes basedon a similarity between the vectorized descriptive string and vectors inthe training set. Particularly, in one embodiment, a training set ofvectors is created from a training set of consumable records havingknown tastes. In one embodiment, the training set of consumable recordsis created based on third-party dataset in which tastes are known. Thetraining set of consumable records is subjected to the vectorization ofthe step 610 to generate the training set of vectors. In an alternativeembodiment, the training set of records and/or vectors may be manuallyentered by a server-side operator. The processing circuitry/logic 204 isconfigured to use a weighted k-nearest neighbor algorithm, which is akind of machine learning pattern recognition, to calculate and provideprobabilities that the consumable has each of the possible tastes as anoutput 640.

For example, if a descriptive string of a consumable record reads “fireranch Doritos”, the vectorization of the descriptive string may be avector that is most similar to the vectors of “spicy” consumables of thetraining set of consumable records. In addition, the vectorization ofthe descriptive string may be somewhat similar to vectors for “sweet”consumables and “salty” consumables of the training set of consumablerecords. Due to these similarities, application of the weightedk-nearest neighbor algorithm to the vectorization of “fire ranchDoritos” may yield a 70% probability of the consumable being spicy, a20% probability of the consumable being salty, and a 10% probability ofthe consumable being sweet.

Nutrient-Based Model

FIG. 7 shows a method 700 of applying a nutrient-based model tocalculate probabilities that a consumable has each of the possibletastes. The method 700 corresponds to one exemplary implementation ofsteps 525 and 535 of the method 500. However, it will be understood by aperson having ordinary skill in the art that other methods and/orvariations of this method are possible.

The method 700 begins with nutritional data of a consumable record as aninput 710. The method 700 includes the step of applying a model to thenutritional data (block 720). Particularly, with respect to theembodiments described in detail herein, the processing circuitry/logic204 of the system server 200 is configured to compare the nutritionaldata to nutritional data of the training set of consumable records andcalculate probabilities that the consumable has each of the possibletastes based on a similarity between the nutritional data and thenutritional data of the training set. Alternatively, the training set ofrecords may be manually entered by an operator at the server-side. Inone embodiment, the training set of consumable records is the sametraining set used with respect to the text-based model, which is basedon a third-party dataset in which tastes are known. In one embodiment,the processing circuitry/logic 204 is configured to compare themacronutrient contents (i.e. carbohydrates, fats, and proteins) of theconsumable to macronutrient contents of the consumable of the trainingset having known tastes. Particularly, the processing circuitry/logic204 is configured to use a weighted k-nearest neighbor algorithm tocalculate and provide probabilities that the consumable has each of thepossible tastes as an output 730.

For example, if a consumable record indicates that a consumable has 15grams of carbohydrates, 5 grams of fat, and 1 gram of protein, then theconsumable may have a macronutrient breakdown that is most similar“sweet” consumables of the training set of consumable records.Additionally, the consumable may have a macronutrient breakdown that issomewhat similar to “salty” consumables of the training set ofconsumable records. Due to these similarities, application of theweighted k-nearest neighbor algorithm to nutritional data may yield a70% probability of the consumable being sweet and a 30% probability ofthe consumable being salty.

Applications for Taste Labels in the Consumable Records Database

In one embodiment, the taste labels associated with the consumablerecords 224 are used to generate a taste profile for a user.Particularly, the processing circuitry/logic 204 of the system server200 is configured to receive a consumable log associated with a userprofile for a user from the user database 222. The consumable logcomprises a plurality of entries corresponding to particular consumablerecords 224 in the database 220. The processing circuitry/logic 204 isconfigured to generate a taste profile for the user based on the tastesof the consumable records corresponding to the plurality of entries inthe consumable log. The processing circuitry/logic 204 is configured tostore the taste profile in the user database 222 in association with theuser profile for the user. Alternatively, the taste profile may besimilarly generated at the client-side, for example by the processor 308of the smartphone 110A. The taste profile includes an estimation of thetaste preferences of the user based on the consumables that the user haslogged. The processing circuitry/logic 204 is configured to transmit thetaste profile to a health tracking device 110 associated with therespective user upon request, for his or her viewing.

In some embodiments, the processing circuitry/logic 204 is configured togenerate additional information based on the taste profile for a user.For example, in one embodiment, the processing circuitry/logic 204 isconfigured to generate and transmit a list of recommended consumablesfor the user. The recommended consumables are consumables that havetastes that align well with the users taste profile, such that theconsumables are likely to be palatable to the user. In one embodiment,the recommended consumables comprise a list of healthier alternatives toone or more consumables that the frequently logs. In this way, the tasteprofile is used to promote healthy choices. In further embodiments, thetaste profiles are used to similarly generate other information such astrends in the taste preferences over time, for different meals, and atdifferent times of the month/year.

In some embodiments, the taste profile is used for advertisementtargeting. Particularly, in one embodiment, the graphics 228 include aplurality of advertisements stored therein. The processingcircuitry/logic 204 is configured to select one or more theadvertisements based on the taste profile of a user. In other words, thetaste profile is used to estimate which of the advertisements is mostrelevant to a particular user. The processing circuitry/logic 204 isconfigured to transmit the selected advertisement the health trackingdevice 110 of the user for presentation to the user. In this way,advertisements may be better targeted to users such that users arepresented with advertisements are of greater relevance to his or hertaste preferences.

In one embodiment, the taste profiles for each user are used fordemographic analysis and improved recommendations. For example, FIG. 8shows exemplary correlations between taste preferences and different agegroups. As may be seen, umami and sour taste perceptions appear to belogged similarly, no matter the user's age. Another trend may be foundin spicy tastes, where the prevalence of spicy foods starts low in the18-25 age group, but then peaks in the young adults aged 26-35 after a7.75% relative increase, before slowly returning to roughly the same asthe initial level in the 65 plus age group. By comparison, the threeremaining tastes are almost strictly increasing or decreasing with age.Salty and bitter foods increase in their observed data by age by 6.92%and 43.22% respectively, while the prevalence of sweet foods decreasesover time by 6.32%. In one embodiment, these observations are used toprovide better recommendations and/or select advertisements targeted tousers based on an age associated with their user profile, as discussedabove.

As another example, FIG. 9 shows exemplary correlations between tastepreferences and observed BMI and gender. As may be seen, there are mixedor weak trends in the tastes of bitter, sour, and spicy. In contrast,there are much stronger and more noticeable trends in umami and saltytastes, where the prevalence of these tastes increases as BMI increases.For umami, there is a 12:72% increase in females from underweight toobese, and a 5.69% increase in males from normal to obese. The trend insalty meanwhile shows a 7.12% increase in females from underweight toobese, and a 9.15% increase in males from normal to obese. In sweettastes, the opposite occurs and average probabilities of sweet decreaseas the user's BMI increases, with a net 11.86% decrease in females fromunderweight to obese, and an 8.90% decrease in males from normal toobese. In one embodiment, these observations are used to provide betterrecommendations and/or select advertisements targeted to users based ona BMI associated with their user profile, as discussed above.

As a further example, FIG. 10 shows exemplary taste preferences acrossmeals. As may be seen, breakfast is observed to have comparativelyhigher proportions of sweet and bitter foods compared to thesecategories' baselines, perhaps reflecting the prevalence of sugaryitems, like cereals or fruit juices, and bitter caffeinated drinks, liketea and coffee, in the morning time. Meanwhile, savory foods dominatelunch and dinner, when more meats may be consumed. In one embodiment,these observations are used to enable better recommendations, targetedadvertisement selections, and understandings of how to reach healthieroutcomes for individuals without upsetting natural meal taste patterns.

As yet another example, FIG. 11 shows exemplary taste preferences acrosscountries. The most significant difference in the taste distributionsbetween these 4 countries corresponds to the high consumption of sweettaste labeled foods among United Kingdom (UK) users, with an averageprobability of 0.44, when compared to United States (US) users with aprobability of 0.37. Furthermore, the consumption of savory foods amongUK users, with an average of 0:34, is the smallest compared to othercountries, i.e., Australia (0:36), US (0:38) and Canada (0:38). Further,more granular taste distributions may also be determined, such as basedon ZIP code, etc. In one embodiment, these observations are used toprovide better recommendations and/or select advertisements targeted tousers based on a location associated with their user profile, asdiscussed above.

It will be appreciated that the various ones of the foregoing aspects ofthe present disclosure, or any parts or functions thereof, may beimplemented using hardware, software, firmware, tangible, andnon-transitory computer readable or computer usable storage media havinginstructions stored thereon, or a combination thereof, and may beimplemented in one or more computer systems.

The above described system and method solves a technological problemcommon in industry practice related to accurately categorizingconsumable items by taste. Moreover, the above-described system andmethod improves the functioning of the computer device by enabling tastedata to be easily presented to a user in association with a consumablerecord in health tracking system, thus also allowing the user to easilyselect consumables that are likely to be of within a typical tasteprofile for the user. In the foregoing description, various operationsmay be described as multiple discrete actions or operations in turn, ina manner that may be helpful in understanding the claimed subjectmatter. However, the order of description should not be construed as toimply that these operations are necessarily order dependent. Inparticular, these operations may not be performed in the order ofpresentation. Operations described may be performed in a different orderthan the described embodiment. Various additional operations may beperformed and/or described operations may be omitted in additionalembodiments.

The foregoing detailed description of one or more exemplary embodimentsof the health tracking system has been presented herein by way ofexample only and not limitation. It will be recognized that there areadvantages to certain individual features and functions described hereinthat may be obtained without incorporating other features and functionsdescribed herein. Moreover, it will be recognized that variousalternatives, modifications, variations, or improvements of theabove-disclosed exemplary embodiments and other features and functions,or alternatives thereof, may be desirably combined into many otherdifferent embodiments, systems or applications. Presently unforeseen orunanticipated alternatives, modifications, variations, or improvementstherein may be subsequently made by those skilled in the art which arealso intended to be encompassed by the appended claims. Therefore, thespirit and scope of any appended claims should not be limited to thedescription of the exemplary embodiments contained herein.

What is claimed is:
 1. A method for operating a health tracking system,the method comprising: receiving a crowd-sourced data record comprisingat least a descriptive string and nutritional data regarding aconsumable item to which the data record corresponds; determining one ofa plurality of possible tastes associated to the consumable item by:evaluating the nutritional data of the data record to determine anaccuracy thereof, when the evaluation of the nutritional data isdetermined to be accurate, applying a first statistical model to thedescriptive string to determine a first set of probabilities that theconsumable item has each of the plurality of possible tastes, applying asecond statistical model to the nutritional data to determine a secondset of probabilities that the consumable item has each of the pluralityof possible tastes, and determining the one of the plurality of possibletastes for the consumable item based on the first set of probabilitiesand the second set of probabilities; and when the nutritional data isdetermined to not be accurate, determining the one of the plurality ofpossible tastes by applying the first statistical model to thedescriptive string to determine the first set of probabilities that theconsumable item has each of the plurality of possible tastes anddetermining the one of a plurality of possible tastes for the consumableitem based only on the first set of probabilities; updating acrowd-sourced database in order to associate the determined taste withthe data record in the crowd-sourced database; performing the acts ofreceiving, determining, and associating with respect to each of aplurality data records stored in the crowd-sourced database such thateach of the plurality of data records is associated to a taste;determining a taste profile specific to a user; generating a list ofrecommended ones of a plurality of consumable items for the user basedat least in part on the determined taste profile specific to the userand the tastes associated with the plurality of data records; andtransmitting the generated list of recommended ones of a plurality ofconsumable items to a health tracking device associated with the user.2. The method according to claim 1, wherein the nutritional dataincludes at least a total caloric content and respective amounts of aplurality of macronutrients of the consumable item, and the act ofdetermining the accuracy further comprises comparing the total caloriccontent to a caloric content representative of the respective amounts ofthe plurality of macronutrients.
 3. The method according to claim 1,further comprising: when the nutritional data is determined to beaccurate, weighting an output of the first statistical model, andweighting an output of the second statistical model.
 4. The methodaccording to claim 3, wherein a value by which the output of the firststatistical model is weighted and a value by which the output of thesecond statistical model is weighted are dependent on a value of theoutput of the first statistical model.
 5. The method according to claim1, further comprising: receiving a list of consumable items, thereceived list being associated with a user profile stored in thedatabase, the received list containing a plurality of entriescorresponding to individual ones of the data records selected by theuser; and associating the determined taste profile to the user profilein the database, wherein the determined taste profile specific to theuser profile is based on one or more patterns determined from the tastesassociated to each of the data records corresponding to the plurality ofentries in the received list.
 6. The method according to claim 5,further comprising: selecting individual ones of a plurality ofadvertisements stored in the database based on the determined tasteprofile; and transmitting the selected individual ones of the pluralityof advertisements to a health tracking device associated with the userprofile.
 7. A health tracking system comprising: a database configuredto store a plurality of crowd-sourced data records, each of theplurality of data records comprising at least a descriptive string andnutritional data regarding the consumable item to which the data recordcorresponds; and a data processor in communication with the database,the data processor being configured to (i) receive one or more of theplurality of data records from the database, (ii) determine a tasteaspect for the consumable item to which each of the received ones of theone or more of the plurality of data records corresponds, thedetermination being based on an evaluation of at least one of thedescriptive string and the nutritional data, (iii) generate a list ofrecommended ones of the plurality of data records based at least in parton the determined taste aspects corresponding to the plurality of datarecords, and (iv) send the generated list of recommended ones of theplurality of data records to a health tracking device associated withthe user; wherein the evaluation of at least one of the descriptivestring and the nutritional data comprises: for each of the one or moreof the plurality of data records, determine whether the nutritional datathereof is accurate; when it is determined that the nutritional data isaccurate, apply a first mathematical model to the nutritional data tocalculate a probability that respective ones of the consumable item havea first taste aspect and based on the result thereof, apply a secondmathematical model to the descriptive string; and when it is determinedthat the nutritional data is not accurate, apply the second mathematicalmodel to the descriptive string to calculate a probability thatrespective ones of the consumable item have the first taste aspect. 8.The health tracking system of claim 7, wherein the data processor isfurther configured to store the determined taste aspects in the databasein association with the respective ones of the one or more of theplurality of data records.
 9. The health tracking system of claim 7,wherein the at least one of the first and second mathematical modelscomprises a machine learning model which utilizes a training set of datarecords having known taste aspects associated therewith.
 10. The healthtracking system of claim 7, wherein at least one of the first and secondmathematical models comprises a weighted k-nearest neighbor algorithm.11. The health tracking system of claim 7, wherein the application ofthe second mathematical model to the descriptive string furthercomprises: vectorize the descriptive string; and apply a patternrecognition model to the vectorized descriptive string.
 12. The healthtracking system of claim 11, wherein the nutritional data includes atleast a total caloric content and respective amounts of a plurality ofmacronutrients of the consumable item, and wherein determining whetherthe nutritional data thereof is accurate further comprises comparing thetotal caloric content to a caloric content representative of respectiveamounts of the plurality of macronutrients.
 13. The health trackingsystem of claim 11, wherein when the nutritional data is determined tobe accurate, weighting an output of the first mathematical model, andweighting an output of the second mathematical model.
 14. The healthtracking system of claim 13, wherein a value by which the output of thefirst statistical model is weighted and a value by which the output ofthe second statistical model is weighted are dependent on a value of theoutput of the first statistical model.
 15. The health tracking system ofclaim 7, wherein the data processor is further configured to: receive alist of consumable items, the received list being associated with a userprofile stored in the database, the received list containing a pluralityof entries corresponding to individual ones of the plurality of datarecords; and associate the determined taste aspect to the user profilein the database, wherein the determined taste aspect specific to theuser profile is based on one or more patterns determined from the tastesassociated to each of the data records corresponding to the plurality ofentries in the received list.
 16. The health tracking system of claim 7,wherein the data processor is further configured to: select individualones of a plurality of advertisements stored in the database based onthe user profile; and transmit the selected individual ones of theplurality of advertisements to a health tracking device associated withthe user profile.
 17. A non-transitory computer-readable medium foroperating a health tracking system, the computer-readable medium havinga plurality of instructions stored thereon that, when executed by aprocessor, cause the processor to: receive a plurality of crowd-sourceddata records from a database, the plurality data records each comprisinga descriptive string and nutritional data regarding the respectivecorresponding consumable; determine a taste for each of the respectivecorresponding consumables of the plurality of data records based on atleast one of (i) the descriptive string of the data record, and (ii) thenutritional data of the data record, the nutritional data including atleast a total caloric content and respective amounts of a plurality ofmacronutrients of the consumable item, wherein the evaluation of thenutritional data of the data record comprises determining the accuracyof the nutritional data by comparing the total caloric content to acaloric content representative of the respective amounts of theplurality of macronutrients, wherein, when the nutritional data isdetermined to be accurate, determining the taste by (a) applying a firststatistical model to the descriptive string and (b) applying a secondstatistical model to the nutritional data; and when the nutritional datais determined to be inaccurate, determining the taste by applying thefirst statistical model to the descriptive string and omitting toutilize the nutritional data; update the database by storing thedetermined tastes in the database associated to the respective ones ofthe plurality of data records; derive a taste profile for a particularuser; and recommend one or more items for consumption and/or provide oneor more advertisements to said particular user based at least in part onthe derived taste profile and the determined taste for each of therespective corresponding consumables.
 18. The non-transitorycomputer-readable medium of claim 12, wherein the plurality ofinstructions are further configured to: utilize said tastes for each ofsaid corresponding consumables to derive the taste profile for theparticular user, the taste profile being based at least in part on oneor more of: individual ones of the plurality of data records selected bythe particular user, an age of the particular user, and/or a gender ofthe particular user.